The second critic is similar to the first critic, but since now we have 2D images of resoltion \(224\times 224\)  representing the rendered human shapes rather than vectors of the SMPL parameters, the images need to be downscaled from resolution \(224\times 224\)  to resolution \(14\times 14\)  before being concatenated with the text embeddings, where the text embeddings are repeated to match the set size and the downscaled resolution of 14. Additionally, the LSTM part consists of 3 fully convolutional gates which further downscale the features until a real-value representing the score is produced. The weight of the Lipschitz term in eq. 1 is set to \(\lambda =10\) . To obtain the rendered shapes, we use an implementation of a differentiable renderer https://github.com/daniilidis-group/neural_renderer, which is based on Neural 3D Mesh Renderer [1]}.
